On the Correctness of Rough-Set Based Approximate Reasoning

  • Patrick Doherty
  • Andrzej Szałas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6086)


There is a natural generalization of an indiscernibility relation used in rough set theory, where rather than partitioning the universe of discourse into indiscernibility classes, one can consider a covering of the universe by similarity-based neighborhoods with lower and upper approximations of relations defined via the neighborhoods. When taking this step, there is a need to tune approximate reasoning to the desired accuracy. We provide a framework for analyzing self-adaptive knowledge structures. We focus on studying the interaction between inputs and output concepts in approximate reasoning. The problems we address are:

  • given similarity relations modeling approximate concepts, what are similarity relations for the output concepts that guarantee correctness of reasoning?

  • assuming that output similarity relations lead to concepts which are not accurate enough, how can one tune input similarities?


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Patrick Doherty
    • 1
  • Andrzej Szałas
    • 1
    • 2
  1. 1.Dept. of Computer and Information ScienceLinköping UniversityLinköpingSweden
  2. 2.Institute of InformaticsWarsaw UniversityWarsawPoland

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